As known in the art, an imaging system may be utilized to implement a known imaging modality (e.g., X-ray, computed tomography, magnetic resonance imaging, ultrasound, positron emission tomography and single-photon emission computed tomography) for generating images of a targeted organ of a patient (e.g., a potentially cancerous organ or an abnormally functioning organ). These images may be utilized by a physician for diagnosis of the patient and/or to plan and execute various treatments of the organ (e.g., image-guided surgery, radiation therapy, etc.). To facilitate an accurate treatment plan for the targeted organ, the targeted organ may need to be segmented for identification and visualization of a contour of the targeted organ within the images.
However, because the image may be difficult to read such as if metal obscures or interferes the anatomy, identification and visualization of the contour of the targeted organ within the image may be impossible or error-prone. Image segmentation typically requires a highly-trained physician to select various points on the surface of the targeted organ to electronically paint the contour of the targeted organ. This can be time consuming and prone to error. More particularly, a demarcation of the boundary between an organ and internal fluids may be difficult due to poor visualization of the organ. A contrast material may be used to help highlight particular anatomy, although some people are sensitive to the contrast.
Alternatively, an automatic segmentation program may be utilized, such as, for example, a boundary reparameterization method disclosed by U.S. Patent Application Publication 20080008369 A1. However, as recognized by the aforementioned publication, the boundaries of the targeted organ may be difficult to identify for various reasons including being masked by the presence of speckle noise, appearing weak in the images due to shading by overlying features and false edges formed by two regions of different gray levels or as the edge between two different textures, or as a hybrid of the two. This complexity leads to high failure rates for image-based automatic segmentation algorithms.